|
on Computational Economics |
|
Issue of 2022‒03‒07
35 papers chosen by |
| By: | Taylan Kabbani (Ozyegin University; Huawei Turkey R&D Center); Fatih Enes Usta (Marmara University) |
| Abstract: | Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend feature as our label to be predicted. Different features are given to help the machine learning model predict the label of a given day; whether it is an uptrend or downtrend, those features are technical indicators generated from the stock's price history. In addition, as financial news plays a vital role in changing the investor's behavior, the overall sentiment score on a given day is created from all news released on that day and added to the model as another feature. Three different machine learning models are tested in Spark (big-data computing platform), Logistic Regression, Random Forest, and Gradient Boosting Machine. Random Forest was the best performing model with a 63.58% test accuracy. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12283&r= |
| By: | Ron Kaniel; Zihan Lin; Markus Pelger; Stijn Van Nieuwerburgh |
| Abstract: | We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum. |
| JEL: | G0 G11 G23 G5 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29723&r= |
| By: | Carmina Fjellstr\"om |
| Abstract: | Performance forecasting is an age-old problem in economics and finance. Recently, developments in machine learning and neural networks have given rise to non-linear time series models that provide modern and promising alternatives to traditional methods of analysis. In this paper, we present an ensemble of independent and parallel long short-term memory (LSTM) neural networks for the prediction of stock price movement. LSTMs have been shown to be especially suited for time series data due to their ability to incorporate past information, while neural network ensembles have been found to reduce variability in results and improve generalization. A binary classification problem based on the median of returns is used, and the ensemble's forecast depends on a threshold value, which is the minimum number of LSTMs required to agree upon the result. The model is applied to the constituents of the smaller, less efficient Stockholm OMX30 instead of other major market indices such as the DJIA and S&P500 commonly found in literature. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time. Moreover, the LSTM portfolio also exhibits less volatility, leading to higher risk-return ratios. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08218&r= |
| By: | Krzysztof Rusek; Piotr Bory{\l}o; Piotr Jaglarz; Fabien Geyer; Albert Cabellos; Piotr Cho{\l}da |
| Abstract: | We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab\'asi-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12263&r= |
| By: | Rahal, Charles; Verhagen, Mark D.; Kirk, David (University of Oxford) |
| Abstract: | This short perspectives-style article explains recent trends and outlines three reasons to be even more optimistic about the future of Machine Learning in the academic Social Sciences. |
| Date: | 2021–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:gydve&r= |
| By: | Schmidt, Lorenz; Odening, Martin; Schlanstein, Johann; Ritter, Matthias |
| Abstract: | Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level wheat yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in estimation precision by using machine learning techniques compared with traditional estimation approaches is quite substantial and that the use of regionalized models and high-resolution weather data improve the performance of ANN. |
| Keywords: | Production Economics, Research Methods / Statistical Methods |
| Date: | 2021–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:ags:gewi21:317075&r= |
| By: | Chaofan Sun; Ken Seng Tan; Wei Wei |
| Abstract: | The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the Mark-to-market value of a financial claim. In contrast to the risk-free closeout, the replacement closeout renders a nonlinear valuation system, which constitutes the major difficulty in the valuation of the counterparty credit risk. In this paper, we show how to address the nonlinearity attributed to the replacement closeout in the theoretical and computational analysis. In the theoretical part, we prove the unique solvability of the nonlinear valuation system and study the impact of the replacement closeout on the credit valuation adjustment. In the computational part, we propose a neural network-based algorithm for solving the (high dimensional) nonlinear valuation system and effectively alleviating the curse of dimensionality. We numerically compare the computational cost for the valuations with risk-free and replacement closeouts. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm for the valuation of the replacement closeout. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09105&r= |
| By: | Zhe Wang; Nicolas Privault; Claude Guet |
| Abstract: | We present an algorithm for the calibration of local volatility from market option prices through deep self-consistent learning, by approximating market option prices and local volatility using deep neural networks. Our method uses the initial-boundary value problem of the underlying Dupire's partial differential equation solved by the parameterized option prices to bring corrections to the parameterization in a self-consistent way. By exploiting the differentiability of the neural networks, we can evaluate Dupire's equation locally at each maturity-strike pair; while by exploiting their continuity, we sample maturity-strike pairs uniformly from a given domain, going beyond the discrete points where the options are quoted. For comparison with existing approaches, the proposed method is tested on both synthetic and market option prices, which shows an improved performance in terms of repricing error, no violation of the no-arbitrage constraints, and smoothness of the calibrated local volatility. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.07880&r= |
| By: | Shuo Sun; Rundong Wang; Xu He; Junlei Zhu; Jian Li; Bo An |
| Abstract: | Reinforcement learning (RL) techniques have shown great success in quantitative investment tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating values. However, it is hard to apply existing RL methods to intraday trading due to the following three limitations: 1) overlooking micro-level market information (e.g., limit order book); 2) only focusing on local price fluctuation and failing to capture the overall trend of the whole trading day; 3) neglecting the impact of market risk. To tackle these limitations, we propose DeepScalper, a deep reinforcement learning framework for intraday trading. Specifically, we adopt an encoder-decoder architecture to learn robust market embedding incorporating both macro-level and micro-level market information. Moreover, a novel hindsight reward function is designed to provide the agent a long-term horizon for capturing the overall price trend. In addition, we propose a risk-aware auxiliary task by predicting future volatility, which helps the agent take market risk into consideration while maximizing profit. Finally, extensive experiments on two stock index futures and four treasury bond futures demonstrate that DeepScalper achieves significant improvement against many state-of-the-art approaches. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.09058&r= |
| By: | Koch, Bernard; Sainburg, Tim; Geraldo, Pablo; JIANG, SONG; Sun, Yizhou; Foster, Jacob G. |
| Abstract: | This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Caus al-Inference. |
| Date: | 2021–10–10 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:aeszf&r= |
| By: | Stephan Martin |
| Abstract: | Nonparametric random coefficient (RC)-density estimation has mostly been considered in the marginal density case under strict independence of RCs and covariates. This paper deals with the estimation of RC-densities conditional on a (large-dimensional) set of control variables using machine learning techniques. The conditional RC-density allows to disentangle observable from unobservable heterogeneity in partial effects of continuous treatments adding to a growing literature on heterogeneous effect estimation using machine learning. %It is also informative of the conditional potential outcome distribution. This paper proposes a two-stage sieve estimation procedure. First a closed-form sieve approximation of the conditional RC density is derived where each sieve coefficient can be expressed as conditional expectation function varying with controls. Second, sieve coefficients are estimated with generic machine learning procedures and under appropriate sample splitting rules. The $L_2$-convergence rate of the conditional RC-density estimator is derived. The rate is slower by a factor then typical rates of mean regression machine learning estimators which is due to the ill-posedness of the RC density estimation problem. The performance and applicability of the estimator is illustrated using random forest algorithms over a range of Monte Carlo simulations and with real data from the SOEP-IS. Here behavioral heterogeneity in an economic experiment on portfolio choice is studied. The method reveals two types of behavior in the population, one type complying with economic theory and one not. The assignment to types appears largely based on unobservables not available in the data. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08366&r= |
| By: | Daoud, Adel; Jordan, Felipe; Sharma, Makkunda; Johansson, Fredrik; Dubhashi, Devdatt; Paul, Sourabh; Banerjee, Subhashis |
| Abstract: | The application of deep learning methods to survey human development in remote areas with satellite imagery at high temporal frequency can significantly enhance our understanding of spatial and temporal patterns in human development. Current applications have focused their efforts in predicting a narrow set of asset-based measurements of human well-being within a limited group of African countries. Here, we leverage georeferenced village-level census data from across 30 percent of the landmass of India to train a deep-neural network that predicts 16 variables representing material conditions from annual composites of Landsat 7 imagery. The census-based model is used as a feature extractor to train another network that predicts an even larger set of developmental variables (over 90 variables) included in two rounds of the National Family Health Survey (NFHS) survey. The census-based model outperforms the current standard in the literature, night-time-luminosity-based models, as a feature extractor for several of these large set of variables. To extend the temporal scope of the models, we suggest a distribution-transformation procedure to estimate outcomes over time and space in India. Our procedure achieves levels of accuracy in the R-square of 0.92 to 0.60 for 21 development outcomes, 0.59 to 0.30 for 25 outcomes, and 0.29 to 0.00 for 28 outcomes, and 19 outcomes had negative R-square. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep learning models that track human development at an unprecedented geographical and temporal definition. |
| Date: | 2021–12–14 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:vf28g&r= |
| By: | Verhagen, Mark D. |
| Abstract: | `All models are wrong, but some are useful' is an often-used mantra, particularly when a model's ability to capture the full complexities of social life is questioned. However, an appropriate functional form is key to valid statistical inference, and under-estimating complexity can lead to biased results. Unfortunately, it is unclear a-priori what the appropriate complexity of a functional form should be. I propose to use methods from machine learning to identify the appropriate complexity of the functional form by i) generating an estimate of the fit potential of the outcome given a set of explanatory variables, ii) comparing this potential with the fit from the functional form originally hypothesized by the researcher, and iii) in case a lack of fit is identified, using recent advances in the field of explainable AI to generate understanding into the missing complexity. I illustrate the approach with a range of simulation and real-world examples. |
| Date: | 2021–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:bka76&r= |
| By: | Celine de Quatrebarbes (FERDI - Fondation pour les Etudes et Recherches sur le Développement International); Bertrand Laporte (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne); Stéphane Calipel (CERDI - Centre d'Études et de Recherches sur le Développement International - CNRS - Centre National de la Recherche Scientifique - UCA - Université Clermont Auvergne) |
| Abstract: | As happened in West Africa in 2008, in an imported inflation context, it is common for the governments to take short-term tax action to protect the poor: VAT or trade tariffs exemptions. As part of the tax-tariff transition, the comparison between Trade tariffs and VAT has already been the subject of much works. The introduction of VAT, as a tax on final consumption, is supposed to be optimal, due to its economically neutral aspect for production decisions. However, some authors show that in developing countries, a large informal sector affects this result. In this paper, we use a CGE model and a micro-simulation model to compare the effects of VAT and Trade tariffs exemptions to combat rising agricultural food prices. The approach is innovative because it integrates how VAT works in developing countries (VAT increases production costs for some producers), in a context of imperfect competition for the service of marketing products. Results show that VAT exemptions are more effective than Trade tariffs exemptions in limiting the effects of the rise in world prices on poverty in Niger. In the context of the current increase in food prices linked to the Covid-19 crisis (FAO, 2020), this issues may one again be in the limelight for the African governments. |
| Keywords: | Computable general equilibrium model,Imperfect competition,Indirect taxes,Poverty,Niger |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cdiwps:hal-03138369&r= |
| By: | Thompson, Jason; Zhao, Haifeng; Seneviratne, Sachith; Byrne, Rohan; Vidanaarachichi, Rajith; McClure, Roderick |
| Abstract: | The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository. |
| Date: | 2021–11–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:sqy8c&r= |
| By: | Ivan Letteri; Giuseppe Della Penna; Giovanni De Gasperis; Abeer Dyoub |
| Abstract: | Stock market forecasting is a lucrative field of interest with promising profits but not without its difficulties and for some people could be even causes of failure. Financial markets by their nature are complex, non-linear and chaotic, which implies that accurately predicting the prices of assets that are part of it becomes very complicated. In this paper we propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market, of the shares issued by Abercrombie & Fitch Co. (ANF) in the stock market of the New York Stock Exchange (NYSE). The system we have elaborated calculates the most effective technical indicator, applying it to the predictions computed by the DNNs, for generating trades. The results showed an increase in values such as Expectancy Ratio of 2.112% of profitable trades with Sharpe, Sortino, and Calmar Ratios of 2.194, 3.340, and 12.403 respectively. As a verification, we adopted a backtracking simulation module in our system, which maps trades to actual test data consisting of the last 30 days of open market on the ANF asset. Overall, the results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100. This was possible because the system reduced the number of trades by choosing the most effective and efficient trades, saving on commissions and slippage costs. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12286&r= |
| By: | Bruno Mazorra; Victor Adan; Vanesa Daza |
| Abstract: | Uniswap, like other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also makes it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already existed in traditional finance but has become more relevant in DeFi. Various projects such as [34,37] have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made in [44]. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their data set by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in Uniswap protocol. We propose various machine-learning-based algorithms with new relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained an accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.07220&r= |
| By: | Beck, Ben; Winters, Meghan; Nelson, Trisalyn; Pettit, Christopher; Saberi, Meead; Thompson, Jason; Seneviratne, Sachith; Nice, Kerry A; Zarpelon-Leao, Simone; Stevenson, Mark |
| Abstract: | Background: Extensive research has been conducted exploring associations of built environment characteristics and biking. However, these approaches have often lacked the ability to understanding the interactions of built environment, population and bicycle ridership. To overcome these limitations, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods. Methods: We conducted a retrospective analysis of travel surveys, bicycle infrastructure and population and land use characteristics in the Greater Melbourne region, Australia. To develop the urban biking typology, we used a k-medoids clustering method. Results: Analyses revealed 5 clusters. We highlight areas with high bicycle network density and a high proportion of trips made by bike (Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and areas with high off-road and on-road bicycle network length, but a low proportion of trips made by bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips). Conclusion: Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further research is required to understand the generalisability of these findings to other settings. |
| Date: | 2021–11–25 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:8w7bg&r= |
| By: | Körtner, John (University of Lausanne); Bonoli, Giuliano |
| Abstract: | With the growing availability of digital administrative data and the recent advances in machine learning, the use of predictive algorithms in the delivery of labour market policy is becoming more prevalent. In public employment services (PES), predictive algorithms are used to support the classification of jobseekers based on their risk of long-term unem- ployment (profiling), the selection of beneficial active labour market programs (targeting), and the matching of jobseekers to suitable job opportunities (matching). In this chapter, we offer a conceptual introduction to the applications of predictive algorithms for the different functions PES have to fulfil and review the history of their use up to the current state of the practice. In addition, we discuss two issues that are inherent to the use of predictive algorithms: algorithmic fairness concerns and the importance of considering how caseworkers will interact with algorithmic systems and make decisions based on their predictions. |
| Date: | 2021–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:j7r8y&r= |
| By: | Thackway, William; Ng, Matthew Kok Ming (University of New South Wales); Lee, Chyi Lin; Pettit, Christopher |
| Abstract: | In an era of rapid urbanisation and increasing wealth, gentrification is an urban phenomenon impacting many cities around the world. The ability of policymakers and planners to better understand and address gentrification-induced displacement hinges upon proactive intervention strategies. It is in this context that we build a tree-based machine learning (ML) model to predict neighbourhood change in Sydney. Change, in this context, is proxied by the Socioeconomic Index for Advantage and Disadvantage, in addition to census and other ancillary predictors. Our models predict gentrification from 2011-2016 with a balanced accuracy of 74.7%. Additionally, the use of an additive explanation tool enables individual prediction explanations and advanced feature contribution analysis. Using the ML model, we predict future gentrification in Sydney up to 2021. The predictions confirm that gentrification is expanding outwards from the city centre. A spill-over effect is predicted to the south, west and north-west of former gentrifying hotspots. The findings are expected to provide policymakers with a tool to better forecast where likely areas of gentrification will occur. This future insight can then inform suitable policy interventions and responses in planning for more equitable cities outcomes, specifically for vulnerable communities impacted by gentrification and neighbourhood change. |
| Date: | 2021–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:hkc96&r= |
| By: | Kresova, Svetlana; Hess, Sebastian |
| Abstract: | Drivers of regional milk price differences across Russian regions are difficult to determine due to limited data availability and restrictions on data collection. In this study, official data from Russian regions for the period from 2013 to 2018 was analysed based on 18 predictor variables in order to explain the regional raw milk price. Due to various data-based restrictions, the use of conventional panel regression models was limited and the analysis was therefore performed based on a Random Forest (RF) machine learning algorithm. Model training and hyperparameter optimization was performed on the training data set with time folds cross-validation. The findings of the study showed that the RF algorithm has a good predictive performance in the test data set even with the default RF values. Finally, the RF variable importance showed that income, gross regional product, livestock density, and milk yield are the four most important variables for explaining the variation in regional milk prices. |
| Keywords: | Agribusiness, International Development, Livestock Production/Industries |
| Date: | 2021–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:ags:gewi21:317051&r= |
| By: | Daniel Levy (Department of Economics, Bar-Ilan University, Israel; Department of Economics, Emory University, US; ICEA, Wilfrid Laurier University, Canada; Rimini Centre for Economic Analysis; ISET, TSU, Georgia); Tamir Mayer (Graduate School of Business Administration, Bar-Ilan University, Israel); Alon Raviv (Graduate School of Business Administration, Bar-Ilan University, Israel) |
| Abstract: | We study the economics and finance scholars' reaction to the 2008 financial crisis using machine learning language analyses methods of Latent Dirichlet Allocation and dynamic topic modelling algorithms, to analyze the texts of 14,270 NBER working papers covering the 1999–2016 period. We find that academic scholars as a group were insufficiently engaged in crises' studies before 2008. As the crisis unraveled, however, they switched their focus to studying the crisis, its causes, and consequences. Thus, the scholars were “slow-to-see,” but they were “fast-to-act.” Their initial response to the ongoing Covid-19 crisis is consistent with these conclusions. |
| Keywords: | Financial crisis, Economic Crisis, Great recession, NBER working papers, LDA textual analysis, Topic modeling, Dynamic Topic Modeling, Machine learning |
| JEL: | E32 E44 E50 F30 G01 G20 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:22-04&r= |
| By: | Akey, Pat; Grégoire, Vincent (HEC Montréal); Martineau, Charles (University of Toronto) |
| Abstract: | From 2010 to 2015, a group of traders illegally accessed earnings information before their public release by hacking several newswire services. We use this scheme as a natural experiment to investigate how informed investors select among private signals and how efficiently financial markets incorporate private information contained in trades into prices. We construct a measure of qualitative information using machine learning and find that the hackers traded on both qualitative and quantitative signals. The hackers’ trading caused 15% more of the earnings news to be incorporated in prices before their public release. Liquidity providers responded to the hackers’ trades by widening spreads. |
| Date: | 2021–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:qe6tu&r= |
| By: | Takanobu Mizuta |
| Abstract: | Commodity trading advisors (CTAs), who mainly trade commodity futures, showed good returns in the 2000s. However, since the 2010's, they have not performed very well. One possible reason of this phenomenon is the emergence of short-term reversal traders (STRTs) who prey on CTAs for profit. In this study, I built an artificial market model by adding a CTA agent (CTAA) and STRT agent (STRTA) to a prior model and investigated whether emerging STRTAs led to a decrease in CTAA revenue to determine whether STRTs prey on CTAs for profit. To the contrary, my results showed that a CTAA and STRTA are more likely to trade and earn more when both exist. Therefore, it is possible that they have a mutually beneficial relationship. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01423&r= |
| By: | Prendergast, Michael |
| Abstract: | This paper describes a methodology for estimating safe withdrawal rates during retirement that is based on a retiree’s age, risk tolerance and investment strategy, and then provides results obtained from using that methodology. The estimates are generated by a three-step process. In the first step, Monte Carlo simulations of future inflation rates, 10-year treasury rates, corporate bond rates (AAA and BAA), the S&P 500 index values and S&P 500 dividend yields are performed. In the second step, portfolio composition and withdrawal rate combinations are evaluated against each of the Monte Carlo simulations in order to calculate portfolio longevity likelihood tables, which are tables that show the likelihood that a portfolio will survive a certain number of years for a given withdrawal rate. In the third and final step, portfolio longevity tables are compared with standard mortality tables in order to estimate the likelihood that the portfolio outlasts the retiree. This three-step approach was then applied using both a Monte Carlo random walk model and an ARIMA/GARCH model based upon over 100 years of monthly historical data. The end result was estimates of the likelihood of portfolio survival to mortality for over 500,000 retiree age/sex/portfolio/withdrawal rate combinations, each combination supported by at least 10,000 Monte Carlo economic simulation points per model. Both models are supported by 100 years of historical data. The first model is a random walk with step sizes determined by bootstrapping, and the second is an ARIMA/GARCH regression model. This data is analyzed to predict safe withdrawal rates and portfolio composition strategies appropriate for the late 2021 economic environment. |
| Date: | 2022–01–20 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:jd2xg&r= |
| By: | Takanobu Mizuta; Isao Yagi; Kosei Takashima |
| Abstract: | Most finance studies are discussed on the basis of several hypotheses, for example, investors rationally optimize their investment strategies. However, the hypotheses themselves are sometimes criticized. Market impacts, where trades of investors can impact and change market prices, making optimization impossible. In this study, we built an artificial market model by adding technical analysis strategy agents searching one optimized parameter to a whole simulation run to the prior model and investigated whether investors' inability to accurately estimate market impacts in their optimizations leads to optimization instability. In our results, the parameter of investment strategy never converged to a specific value but continued to change. This means that even if all other traders are fixed, only one investor will use backtesting to optimize his/her strategy, which leads to the time evolution of market prices becoming unstable. Optimization instability is one level higher than "non-equilibrium of market prices." Therefore, the time evolution of market prices produced by investment strategies having such unstable parameters is highly unlikely to be predicted and have stable laws written by equations. This nature makes us suspect that financial markets include the principle of natural uniformity and indicates the difficulty of building an equation model explaining the time evolution of prices. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00831&r= |
| By: | Ihsan Chaoubi; Camille Besse; H\'el\`ene Cossette; Marie-Pier C\^ot\'e |
| Abstract: | Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.13267&r= |
| By: | Martin Guth |
| Abstract: | Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of different regression models to find the best performing credit risk model. We set up an estimation framework that allows us to systematically estimate and evaluate a large set of models within the same environment. Our results indicate that there are indeed better performing models than the current state-of-the-art model. Moreover, our comparison sheds light on other potential credit risk models, specifically highlighting the advantages of machine learning models and forecast combinations. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.03110&r= |
| By: | Zhiqin Zou (China University of Petroleum); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles); Tom Mcnamara (Rennes School of Business) |
| Abstract: | In order to implement or maintain a green supply chain (GSC) that produces goods and services responsibly and sustainably, supply chain managers should use tools that allow for the efficient identification, quantification, and mitigation of the ever‐present risks. The objective of the present research is to identify the risk factors associated with the processes involved in GSC management. Based on an analysis of the characteristics of GSC risk, the authors put forward a list of risk design principles and a risk criteria evaluation system for a GSC. Gray relation analysis method was then used to clarify the degree of connection between certain supply chain risk factors and select key risk factors. Finally, Back Propagation Artificial Neural Network (BP‐ANN) method was used to determine the risk level associated with a GSC. The determination of risk level will help companies to develop effective strategic management initiatives in a GSC environment. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03181313&r= |
| By: | Jori Hoencamp; Shashi Jain; Drona Kandhai |
| Abstract: | We present a semi-static hedging algorithm for callable interest rate derivatives under an affine, multi-factor term-structure model. With a traditional dynamic hedge, the replication portfolio needs to be updated continuously through time as the market moves. In contrast, we propose a semi-static hedge that needs rebalancing on just a finite number of instances. We show, taking as an example Bermudan swaptions, that callable interest rate derivatives can be replicated with an options portfolio written on a basket of discount bonds. The static portfolio composition is obtained by regressing the target option's value using an interpretable, artificial neural network. Leveraging on the approximation power of neural networks, we prove that the hedging error can be arbitrarily small for a sufficiently large replication portfolio. A direct, a lower bound, and an upper bound estimator for the risk-neutral Bermudan swaption price is inferred from the hedging algorithm. Additionally, closed-form error margins to the price statistics are determined. We practically demonstrate the hedging and pricing performance through several numerical experiments. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.01027&r= |
| By: | Koichi Miyamoto |
| Abstract: | Finance is one of the promising field for industrial application of quantum computing. In particular, quantum algorithms for calculation of risk measures such as the value at risk and the conditional value at risk of a credit portfolio have been proposed. In this paper, we focus on another problem in credit risk management, calculation of risk contributions, which quantify the concentration of the risk on subgroups in the portfolio. Based on the recent quantum algorithm for simultaneous estimation of multiple expected values, we propose the method for credit risk contribution calculation. We also evaluate the query complexity of the proposed method and see that it scales as $\widetilde{O}\left(\sqrt{N_{\rm gr}}/\epsilon\right)$ on the subgroup number $N_{\rm gr}$ and the accuracy $\epsilon$, in contrast with the classical method with $\widetilde{O}\left(\log(N_{\rm gr})/\epsilon^2\right)$ complexity. This means that, for calculation of risk contributions of finely divided subgroups, the advantage of the quantum method is reduced compared with risk measure calculation for the entire portfolio. Nevertheless, the quantum method can be advantageous in high-accuracy calculation, and in fact yield less complexity than the classical method in some practically plausible setting. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11394&r= |
| By: | Morteza Taiebat; Elham Amini; Ming Xu |
| Abstract: | Ride-hailing is rapidly changing urban and personal transportation. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year, while the trip volume and mileage have remained statistically unchanged. We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo. Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to 95% and 91% of the predictive power in determining whether a trip is requested to share and whether it is successfully shared, respectively. Spatial and temporal attributes, sociodemographic, built environment, and transit supply variables do not entail predictive power at the trip level in presence of these travel impedance variables. This implies that pricing signals are most effective to encourage riders to share their rides. Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing, especially as the demand recovers from pandemic. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12696&r= |
| By: | Yulin Liu; Luyao Zhang |
| Abstract: | Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns. We verify PU ratio valuation by unsupervised and supervised machine learning. The valuation method informs investment returns and predicts bull markets effectively. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. We distribute the trading algorithms as open-source software via Python Package Index for future research. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12893&r= |
| By: | Gabriel Okasa |
| Abstract: | Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic simulations we find that the performance of the meta-learners in finite samples greatly depends on the estimation procedure. The results imply that sample-splitting and cross-fitting are beneficial in large samples for bias reduction and efficiency of the meta-learners, respectively, whereas full-sample estimation is preferable in small samples. Furthermore, we derive practical recommendations for application of specific meta-learners in empirical studies depending on particular data characteristics such as treatment shares and sample size. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12692&r= |
| By: | Igor Nesiolovskiy |
| Abstract: | This paper proposes a method for ranking the investment attractiveness of exchange-traded stocks where investment risk is not related to the volatility indicator but instead is related to the indicator of compression of the time series of price changes. The article describes in detail the ranking algorithm, provides an example of ranking the shares of all companies included in the Dow Jones stock index. The paper additionally compares the results of ranking these stocks by volatility and compression and also shows the strengths of the second indicator, which is formed using the method of binary-ternary compression of historical financial data. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.11507&r= |